library(here)
library(tidyverse)
theme_set(theme_light())

library(scuttle)

library(kableExtra)
library(DDCompanion)

Setup

## Directory setup
here_root <- "benchmarks/lupus-n_patients"
here::i_am(file.path(here_root, "analysis/lupus-n_patients-mock-results.Rmd"))
#> here() starts at /Users/jg/Desktop/PhD/DD_project/DD_git

res_dir <- here::here(here_root, "results")
fig_dir <- here::here(here_root, "figures")
  • Using n_patients: 10 20 30
  • Using methods: edgeR_QP, edgeR_NB, qbGLM_offset_squeeze, bGLM, qbGLM, qbGLM_offset
  • Using celltype: T4_naive

Load results

# TODO: also add all-patients results for comparison?



res_files <- map(n_patients, ~ get_mock_res_files(
    dataset = "lupus-n_patients",
    methods = methods, celltype = celltype, datatype = "pb",
    n_patients = .x
)) %>%
    set_names(paste0("n_patients_", n_patients))

res_list <- map_depth(res_files, 2, readRDS)
str(res_list, max.level = 3)
#> List of 3
#>  $ n_patients_10:List of 6
#>   ..$ edgeR_QP            :List of 5
#>   .. ..$ replicate_1:List of 2
#>   .. ..$ replicate_2:List of 2
#>   .. ..$ replicate_3:List of 2
#>   .. ..$ replicate_4:List of 2
#>   .. ..$ replicate_5:List of 2
#>   ..$ edgeR_NB            :List of 5
#>   .. ..$ replicate_1:List of 2
#>   .. ..$ replicate_2:List of 2
#>   .. ..$ replicate_3:List of 2
#>   .. ..$ replicate_4:List of 2
#>   .. ..$ replicate_5:List of 2
#>   ..$ qbGLM_offset_squeeze:List of 5
#>   .. ..$ replicate_1:List of 2
#>   .. ..$ replicate_2:List of 2
#>   .. ..$ replicate_3:List of 2
#>   .. ..$ replicate_4:List of 2
#>   .. ..$ replicate_5:List of 2
#>   ..$ bGLM                :List of 5
#>   .. ..$ replicate_1:List of 2
#>   .. ..$ replicate_2:List of 2
#>   .. ..$ replicate_3:List of 2
#>   .. ..$ replicate_4:List of 2
#>   .. ..$ replicate_5:List of 2
#>   ..$ qbGLM               :List of 5
#>   .. ..$ replicate_1:List of 2
#>   .. ..$ replicate_2:List of 2
#>   .. ..$ replicate_3:List of 2
#>   .. ..$ replicate_4:List of 2
#>   .. ..$ replicate_5:List of 2
#>   ..$ qbGLM_offset        :List of 5
#>   .. ..$ replicate_1:List of 2
#>   .. ..$ replicate_2:List of 2
#>   .. ..$ replicate_3:List of 2
#>   .. ..$ replicate_4:List of 2
#>   .. ..$ replicate_5:List of 2
#>  $ n_patients_20:List of 6
#>   ..$ edgeR_QP            :List of 5
#>   .. ..$ replicate_1:List of 2
#>   .. ..$ replicate_2:List of 2
#>   .. ..$ replicate_3:List of 2
#>   .. ..$ replicate_4:List of 2
#>   .. ..$ replicate_5:List of 2
#>   ..$ edgeR_NB            :List of 5
#>   .. ..$ replicate_1:List of 2
#>   .. ..$ replicate_2:List of 2
#>   .. ..$ replicate_3:List of 2
#>   .. ..$ replicate_4:List of 2
#>   .. ..$ replicate_5:List of 2
#>   ..$ qbGLM_offset_squeeze:List of 5
#>   .. ..$ replicate_1:List of 2
#>   .. ..$ replicate_2:List of 2
#>   .. ..$ replicate_3:List of 2
#>   .. ..$ replicate_4:List of 2
#>   .. ..$ replicate_5:List of 2
#>   ..$ bGLM                :List of 5
#>   .. ..$ replicate_1:List of 2
#>   .. ..$ replicate_2:List of 2
#>   .. ..$ replicate_3:List of 2
#>   .. ..$ replicate_4:List of 2
#>   .. ..$ replicate_5:List of 2
#>   ..$ qbGLM               :List of 5
#>   .. ..$ replicate_1:List of 2
#>   .. ..$ replicate_2:List of 2
#>   .. ..$ replicate_3:List of 2
#>   .. ..$ replicate_4:List of 2
#>   .. ..$ replicate_5:List of 2
#>   ..$ qbGLM_offset        :List of 5
#>   .. ..$ replicate_1:List of 2
#>   .. ..$ replicate_2:List of 2
#>   .. ..$ replicate_3:List of 2
#>   .. ..$ replicate_4:List of 2
#>   .. ..$ replicate_5:List of 2
#>  $ n_patients_30:List of 6
#>   ..$ edgeR_QP            :List of 5
#>   .. ..$ replicate_1:List of 2
#>   .. ..$ replicate_2:List of 2
#>   .. ..$ replicate_3:List of 2
#>   .. ..$ replicate_4:List of 2
#>   .. ..$ replicate_5:List of 2
#>   ..$ edgeR_NB            :List of 5
#>   .. ..$ replicate_1:List of 2
#>   .. ..$ replicate_2:List of 2
#>   .. ..$ replicate_3:List of 2
#>   .. ..$ replicate_4:List of 2
#>   .. ..$ replicate_5:List of 2
#>   ..$ qbGLM_offset_squeeze:List of 5
#>   .. ..$ replicate_1:List of 2
#>   .. ..$ replicate_2:List of 2
#>   .. ..$ replicate_3:List of 2
#>   .. ..$ replicate_4:List of 2
#>   .. ..$ replicate_5:List of 2
#>   ..$ bGLM                :List of 5
#>   .. ..$ replicate_1:List of 2
#>   .. ..$ replicate_2:List of 2
#>   .. ..$ replicate_3:List of 2
#>   .. ..$ replicate_4:List of 2
#>   .. ..$ replicate_5:List of 2
#>   ..$ qbGLM               :List of 5
#>   .. ..$ replicate_1:List of 2
#>   .. ..$ replicate_2:List of 2
#>   .. ..$ replicate_3:List of 2
#>   .. ..$ replicate_4:List of 2
#>   .. ..$ replicate_5:List of 2
#>   ..$ qbGLM_offset        :List of 5
#>   .. ..$ replicate_1:List of 2
#>   .. ..$ replicate_2:List of 2
#>   .. ..$ replicate_3:List of 2
#>   .. ..$ replicate_4:List of 2
#>   .. ..$ replicate_5:List of 2

Load SCE objects

data_files <- map(n_patients, ~ get_SCE_files(
    dataset = "lupus-n_patients", which = "mock_replicates",
    celltype = celltype, n_patients = .x
)) %>%
    set_names(paste0("n_patients_", n_patients))
sce_objects <- map(data_files, readRDS)
str(sce_objects, max.level = 3)
#> List of 3
#>  $ n_patients_10:List of 5
#>   ..$ replicate_1:Formal class 'SingleCellExperiment' [package "SingleCellExperiment"] with 9 slots
#>   ..$ replicate_2:Formal class 'SingleCellExperiment' [package "SingleCellExperiment"] with 9 slots
#>   ..$ replicate_3:Formal class 'SingleCellExperiment' [package "SingleCellExperiment"] with 9 slots
#>   ..$ replicate_4:Formal class 'SingleCellExperiment' [package "SingleCellExperiment"] with 9 slots
#>   ..$ replicate_5:Formal class 'SingleCellExperiment' [package "SingleCellExperiment"] with 9 slots
#>  $ n_patients_20:List of 5
#>   ..$ replicate_1:Formal class 'SingleCellExperiment' [package "SingleCellExperiment"] with 9 slots
#>   ..$ replicate_2:Formal class 'SingleCellExperiment' [package "SingleCellExperiment"] with 9 slots
#>   ..$ replicate_3:Formal class 'SingleCellExperiment' [package "SingleCellExperiment"] with 9 slots
#>   ..$ replicate_4:Formal class 'SingleCellExperiment' [package "SingleCellExperiment"] with 9 slots
#>   ..$ replicate_5:Formal class 'SingleCellExperiment' [package "SingleCellExperiment"] with 9 slots
#>  $ n_patients_30:List of 5
#>   ..$ replicate_1:Formal class 'SingleCellExperiment' [package "SingleCellExperiment"] with 9 slots
#>   ..$ replicate_2:Formal class 'SingleCellExperiment' [package "SingleCellExperiment"] with 9 slots
#>   ..$ replicate_3:Formal class 'SingleCellExperiment' [package "SingleCellExperiment"] with 9 slots
#>   ..$ replicate_4:Formal class 'SingleCellExperiment' [package "SingleCellExperiment"] with 9 slots
#>   ..$ replicate_5:Formal class 'SingleCellExperiment' [package "SingleCellExperiment"] with 9 slots

Data overview

  • The results were generated from 5 mock replicates
  • Each replicate was generated by randomly splitting the subjects in two mock groups
  • No sub-sampling of cells per patient was performed for this data
  • The number of patients was randomly sub-sampled stratified by mock group

Note that for n_patients 10 and 20, the selected patients vary randomly between the replicates, which is why the nrows and ncols are not identical. For the n_patients 30, however, no random sub-sampling was done because I could just use all patients from 2 out of the original 3 batches from the Control samples. That’s why the nrows and ncols are identical for those subsets.

The mock group assignment of each subject also varies across replicates.

map_dfr(sce_objects,
    ~ map_dfr(.x, function(x) c(nrows = nrow(x), ncols = ncol(x)),
        .id = "replicate"
    ),
    .id = "n_patients"
)

Subjects are divided across mock groups as follows:

n_patients = 10

replicate_1

A B
IGTB1793_IGTB1793 1 0
IGTB1815_IGTB1815 1 0
IGTB1828_IGTB1828 1 0
IGTB1895_IGTB1895 0 1
IGTB1901_IGTB1901 0 1
IGTB1952_IGTB1952 0 1
IGTB1966_IGTB1966 0 1
IGTB1982_IGTB1982 0 1
IGTB2007_IGTB2007 1 0
IGTB2065_IGTB2065 1 0

replicate_2

A B
IGTB1762_IGTB1762 1 0
IGTB1789_IGTB1789 0 1
IGTB1793_IGTB1793 0 1
IGTB1815_IGTB1815 1 0
IGTB1828_IGTB1828 0 1
IGTB1840_IGTB1840 1 0
IGTB1901_IGTB1901 1 0
IGTB1996_IGTB1996 0 1
IGTB2007_IGTB2007 0 1
IGTB2065_IGTB2065 1 0

replicate_3

A B
IGTB1762_IGTB1762 0 1
IGTB1793_IGTB1793 1 0
IGTB1815_IGTB1815 1 0
IGTB1828_IGTB1828 0 1
IGTB1871_IGTB1871 1 0
IGTB1895_IGTB1895 0 1
IGTB1952_IGTB1952 1 0
IGTB1966_IGTB1966 0 1
IGTB1996_IGTB1996 1 0
IGTB2007_IGTB2007 0 1

replicate_4

A B
IGTB1762_IGTB1762 0 1
IGTB1793_IGTB1793 0 1
IGTB1828_IGTB1828 0 1
IGTB1840_IGTB1840 1 0
IGTB1895_IGTB1895 1 0
IGTB1966_IGTB1966 1 0
IGTB1982_IGTB1982 0 1
IGTB1996_IGTB1996 1 0
IGTB2007_IGTB2007 1 0
IGTB2065_IGTB2065 0 1

replicate_5

A B
IGTB1789_IGTB1789 0 1
IGTB1815_IGTB1815 1 0
IGTB1828_IGTB1828 1 0
IGTB1840_IGTB1840 1 0
IGTB1895_IGTB1895 1 0
IGTB1952_IGTB1952 0 1
IGTB1966_IGTB1966 0 1
IGTB1996_IGTB1996 1 0
IGTB2007_IGTB2007 0 1
IGTB2065_IGTB2065 0 1

n_patients = 20

replicate_1

A B
IGTB143_IGTB143 1 0
IGTB469_IGTB469 0 1
IGTB498_IGTB498 0 1
IGTB508_IGTB508 1 0
IGTB514_IGTB514 0 1
IGTB645_IGTB645 0 1
IGTB670_IGTB670 1 0
IGTB826_IGTB826 1 0
IGTB1539_IGTB1539 1 0
IGTB1762_IGTB1762 1 0
IGTB1828_IGTB1828 1 0
IGTB1840_IGTB1840 1 0
IGTB1871_IGTB1871 0 1
IGTB1901_IGTB1901 0 1
IGTB1952_IGTB1952 0 1
IGTB1966_IGTB1966 0 1
IGTB1982_IGTB1982 0 1
IGTB1996_IGTB1996 0 1
IGTB2007_IGTB2007 1 0
IGTB2065_IGTB2065 1 0

replicate_2

A B
IGTB141_IGTB141 1 0
IGTB195_IGTB195 1 0
IGTB469_IGTB469 1 0
IGTB514_IGTB514 0 1
IGTB645_IGTB645 1 0
IGTB670_IGTB670 0 1
IGTB1372_IGTB1372 0 1
IGTB1506_IGTB1506 0 1
IGTB1575_IGTB1575 0 1
IGTB1789_IGTB1789 0 1
IGTB1815_IGTB1815 1 0
IGTB1828_IGTB1828 0 1
IGTB1871_IGTB1871 0 1
IGTB1895_IGTB1895 1 0
IGTB1901_IGTB1901 1 0
IGTB1906_IGTB1906 0 1
IGTB1952_IGTB1952 0 1
IGTB1966_IGTB1966 1 0
IGTB1982_IGTB1982 1 0
IGTB2065_IGTB2065 1 0

replicate_3

A B
IGTB141_IGTB141 1 0
IGTB143_IGTB143 0 1
IGTB195_IGTB195 1 0
IGTB469_IGTB469 0 1
IGTB498_IGTB498 1 0
IGTB514_IGTB514 1 0
IGTB826_IGTB826 0 1
IGTB1372_IGTB1372 1 0
IGTB1575_IGTB1575 0 1
IGTB1762_IGTB1762 0 1
IGTB1789_IGTB1789 1 0
IGTB1815_IGTB1815 1 0
IGTB1828_IGTB1828 0 1
IGTB1840_IGTB1840 1 0
IGTB1901_IGTB1901 0 1
IGTB1906_IGTB1906 0 1
IGTB1952_IGTB1952 1 0
IGTB1966_IGTB1966 0 1
IGTB1982_IGTB1982 0 1
IGTB1996_IGTB1996 1 0

replicate_4

A B
IGTB141_IGTB141 1 0
IGTB143_IGTB143 0 1
IGTB508_IGTB508 1 0
IGTB670_IGTB670 0 1
IGTB1506_IGTB1506 0 1
IGTB1539_IGTB1539 0 1
IGTB1762_IGTB1762 0 1
IGTB1789_IGTB1789 0 1
IGTB1793_IGTB1793 0 1
IGTB1815_IGTB1815 1 0
IGTB1840_IGTB1840 1 0
IGTB1871_IGTB1871 1 0
IGTB1895_IGTB1895 1 0
IGTB1901_IGTB1901 0 1
IGTB1906_IGTB1906 1 0
IGTB1952_IGTB1952 0 1
IGTB1966_IGTB1966 1 0
IGTB1996_IGTB1996 1 0
IGTB2007_IGTB2007 1 0
IGTB2065_IGTB2065 0 1

replicate_5

A B
IGTB141_IGTB141 0 1
IGTB143_IGTB143 0 1
IGTB508_IGTB508 1 0
IGTB514_IGTB514 1 0
IGTB645_IGTB645 1 0
IGTB826_IGTB826 1 0
IGTB1372_IGTB1372 0 1
IGTB1575_IGTB1575 1 0
IGTB1789_IGTB1789 0 1
IGTB1793_IGTB1793 1 0
IGTB1828_IGTB1828 1 0
IGTB1840_IGTB1840 1 0
IGTB1871_IGTB1871 1 0
IGTB1901_IGTB1901 0 1
IGTB1952_IGTB1952 0 1
IGTB1966_IGTB1966 0 1
IGTB1982_IGTB1982 0 1
IGTB1996_IGTB1996 1 0
IGTB2007_IGTB2007 0 1
IGTB2065_IGTB2065 0 1

n_patients = 30

replicate_1

A B
IGTB141_IGTB141 0 1
IGTB143_IGTB143 1 0
IGTB195_IGTB195 1 0
IGTB469_IGTB469 0 1
IGTB498_IGTB498 0 1
IGTB508_IGTB508 1 0
IGTB514_IGTB514 0 1
IGTB645_IGTB645 0 1
IGTB670_IGTB670 1 0
IGTB826_IGTB826 1 0
IGTB1372_IGTB1372 0 1
IGTB1506_IGTB1506 0 1
IGTB1539_IGTB1539 1 0
IGTB1575_IGTB1575 1 0
IGTB1762_IGTB1762 1 0
IGTB1789_IGTB1789 0 1
IGTB1793_IGTB1793 1 0
IGTB1815_IGTB1815 1 0
IGTB1828_IGTB1828 1 0
IGTB1840_IGTB1840 1 0
IGTB1871_IGTB1871 0 1
IGTB1895_IGTB1895 0 1
IGTB1901_IGTB1901 0 1
IGTB1906_IGTB1906 1 0
IGTB1952_IGTB1952 0 1
IGTB1966_IGTB1966 0 1
IGTB1982_IGTB1982 0 1
IGTB1996_IGTB1996 0 1
IGTB2007_IGTB2007 1 0
IGTB2065_IGTB2065 1 0

replicate_2

A B
IGTB141_IGTB141 1 0
IGTB143_IGTB143 1 0
IGTB195_IGTB195 1 0
IGTB469_IGTB469 1 0
IGTB498_IGTB498 1 0
IGTB508_IGTB508 1 0
IGTB514_IGTB514 0 1
IGTB645_IGTB645 1 0
IGTB670_IGTB670 0 1
IGTB826_IGTB826 0 1
IGTB1372_IGTB1372 0 1
IGTB1506_IGTB1506 0 1
IGTB1539_IGTB1539 0 1
IGTB1575_IGTB1575 0 1
IGTB1762_IGTB1762 1 0
IGTB1789_IGTB1789 0 1
IGTB1793_IGTB1793 0 1
IGTB1815_IGTB1815 1 0
IGTB1828_IGTB1828 0 1
IGTB1840_IGTB1840 1 0
IGTB1871_IGTB1871 0 1
IGTB1895_IGTB1895 1 0
IGTB1901_IGTB1901 1 0
IGTB1906_IGTB1906 0 1
IGTB1952_IGTB1952 0 1
IGTB1966_IGTB1966 1 0
IGTB1982_IGTB1982 1 0
IGTB1996_IGTB1996 0 1
IGTB2007_IGTB2007 0 1
IGTB2065_IGTB2065 1 0

replicate_3

A B
IGTB141_IGTB141 1 0
IGTB143_IGTB143 0 1
IGTB195_IGTB195 1 0
IGTB469_IGTB469 0 1
IGTB498_IGTB498 1 0
IGTB508_IGTB508 1 0
IGTB514_IGTB514 1 0
IGTB645_IGTB645 1 0
IGTB670_IGTB670 0 1
IGTB826_IGTB826 0 1
IGTB1372_IGTB1372 1 0
IGTB1506_IGTB1506 1 0
IGTB1539_IGTB1539 0 1
IGTB1575_IGTB1575 0 1
IGTB1762_IGTB1762 0 1
IGTB1789_IGTB1789 1 0
IGTB1793_IGTB1793 1 0
IGTB1815_IGTB1815 1 0
IGTB1828_IGTB1828 0 1
IGTB1840_IGTB1840 1 0
IGTB1871_IGTB1871 1 0
IGTB1895_IGTB1895 0 1
IGTB1901_IGTB1901 0 1
IGTB1906_IGTB1906 0 1
IGTB1952_IGTB1952 1 0
IGTB1966_IGTB1966 0 1
IGTB1982_IGTB1982 0 1
IGTB1996_IGTB1996 1 0
IGTB2007_IGTB2007 0 1
IGTB2065_IGTB2065 1 0

replicate_4

A B
IGTB141_IGTB141 1 0
IGTB143_IGTB143 0 1
IGTB195_IGTB195 0 1
IGTB469_IGTB469 1 0
IGTB498_IGTB498 0 1
IGTB508_IGTB508 1 0
IGTB514_IGTB514 1 0
IGTB645_IGTB645 1 0
IGTB670_IGTB670 0 1
IGTB826_IGTB826 0 1
IGTB1372_IGTB1372 0 1
IGTB1506_IGTB1506 0 1
IGTB1539_IGTB1539 0 1
IGTB1575_IGTB1575 1 0
IGTB1762_IGTB1762 0 1
IGTB1789_IGTB1789 0 1
IGTB1793_IGTB1793 0 1
IGTB1815_IGTB1815 1 0
IGTB1828_IGTB1828 0 1
IGTB1840_IGTB1840 1 0
IGTB1871_IGTB1871 1 0
IGTB1895_IGTB1895 1 0
IGTB1901_IGTB1901 0 1
IGTB1906_IGTB1906 1 0
IGTB1952_IGTB1952 0 1
IGTB1966_IGTB1966 1 0
IGTB1982_IGTB1982 0 1
IGTB1996_IGTB1996 1 0
IGTB2007_IGTB2007 1 0
IGTB2065_IGTB2065 0 1

replicate_5

A B
IGTB141_IGTB141 0 1
IGTB143_IGTB143 0 1
IGTB195_IGTB195 1 0
IGTB469_IGTB469 0 1
IGTB498_IGTB498 1 0
IGTB508_IGTB508 1 0
IGTB514_IGTB514 1 0
IGTB645_IGTB645 1 0
IGTB670_IGTB670 0 1
IGTB826_IGTB826 1 0
IGTB1372_IGTB1372 0 1
IGTB1506_IGTB1506 1 0
IGTB1539_IGTB1539 0 1
IGTB1575_IGTB1575 1 0
IGTB1762_IGTB1762 0 1
IGTB1789_IGTB1789 0 1
IGTB1793_IGTB1793 1 0
IGTB1815_IGTB1815 1 0
IGTB1828_IGTB1828 1 0
IGTB1840_IGTB1840 1 0
IGTB1871_IGTB1871 1 0
IGTB1895_IGTB1895 1 0
IGTB1901_IGTB1901 0 1
IGTB1906_IGTB1906 0 1
IGTB1952_IGTB1952 0 1
IGTB1966_IGTB1966 0 1
IGTB1982_IGTB1982 0 1
IGTB1996_IGTB1996 1 0
IGTB2007_IGTB2007 0 1
IGTB2065_IGTB2065 0 1

Extract results of interest

Runtimes

runtimes <- map_dfr(res_list,
    ~ map_dfr(.x, get_runtimes, depth = 1, .id = "method"),
    .id = "n_patients"
) %>%
    mutate(n_patients = as.numeric(sub("n_patients_", "", n_patients)))

P-values

res_tables <- map_depth(
    res_list, 2,
    ~ get_aggregated_rep_tables(.x, depth = 1)
) %>%
    map(combine_tables, .id = "method")

Visualize results

Run times

ggplot(runtimes, aes(n_patients, time, col = method)) +
    geom_jitter(width = 0.5, alpha = 0.6) +
    geom_smooth(se = FALSE, method = "lm", formula = y ~ x) +
    labs(x = "Number of patients", y = "Time (seconds)", color = NULL) +
    scale_x_continuous(breaks = unique(runtimes$n_patients), minor_breaks = NULL)

Number of significant genes for different FDR levels

n_sign_summaries <- map(res_tables, function(tmp) {
    tmp %>%
        group_by(replicate, method) %>%
        summarise(
            sum(FDR < 0.01),
            sum(FDR < 0.05),
            sum(FDR < 0.1),
            .groups = "drop"
        )
})

n_patients_10

method sum(FDR < 0.01) sum(FDR < 0.05) sum(FDR < 0.1)
Replicate: 1
bGLM 0 2 2
edgeR_NB 0 0 0
edgeR_QP 0 0 0
qbGLM 0 0 0
qbGLM_offset 0 0 0
qbGLM_offset_squeeze 0 0 0
Replicate: 2
bGLM 0 1 2
edgeR_NB 0 0 0
edgeR_QP 0 0 0
qbGLM 0 0 0
qbGLM_offset 0 0 0
qbGLM_offset_squeeze 0 0 0
Replicate: 3
bGLM 0 6 7
edgeR_NB 0 0 0
edgeR_QP 0 0 0
qbGLM 0 0 1
qbGLM_offset 0 1 1
qbGLM_offset_squeeze 0 1 1
Replicate: 4
bGLM 2 4 7
edgeR_NB 0 0 0
edgeR_QP 0 0 0
qbGLM 0 0 0
qbGLM_offset 0 0 0
qbGLM_offset_squeeze 0 0 0
Replicate: 5
bGLM 0 0 0
edgeR_NB 0 0 0
edgeR_QP 0 0 0
qbGLM 0 0 0
qbGLM_offset 0 0 0
qbGLM_offset_squeeze 0 0 0

n_patients_20

method sum(FDR < 0.01) sum(FDR < 0.05) sum(FDR < 0.1)
Replicate: 1
bGLM 0 1 1
edgeR_NB 0 0 0
edgeR_QP 0 0 0
qbGLM 0 0 0
qbGLM_offset 0 0 0
qbGLM_offset_squeeze 0 0 0
Replicate: 2
bGLM 0 1 2
edgeR_NB 0 0 0
edgeR_QP 0 0 0
qbGLM 0 0 0
qbGLM_offset 0 0 0
qbGLM_offset_squeeze 0 0 0
Replicate: 3
bGLM 0 1 1
edgeR_NB 0 0 0
edgeR_QP 0 0 0
qbGLM 0 0 0
qbGLM_offset 0 0 0
qbGLM_offset_squeeze 0 0 0
Replicate: 4
bGLM 1 2 12
edgeR_NB 0 0 0
edgeR_QP 0 0 0
qbGLM 0 0 0
qbGLM_offset 0 0 0
qbGLM_offset_squeeze 0 0 0
Replicate: 5
bGLM 0 0 0
edgeR_NB 0 0 0
edgeR_QP 0 0 0
qbGLM 0 0 0
qbGLM_offset 0 0 0
qbGLM_offset_squeeze 0 0 0

n_patients_30

method sum(FDR < 0.01) sum(FDR < 0.05) sum(FDR < 0.1)
Replicate: 1
bGLM 0 0 0
edgeR_NB 0 0 0
edgeR_QP 0 0 0
qbGLM 0 1 1
qbGLM_offset 0 0 1
qbGLM_offset_squeeze 0 0 2
Replicate: 2
bGLM 1 6 8
edgeR_NB 0 0 0
edgeR_QP 0 0 0
qbGLM 0 0 0
qbGLM_offset 0 0 0
qbGLM_offset_squeeze 0 0 0
Replicate: 3
bGLM 0 0 0
edgeR_NB 0 0 0
edgeR_QP 0 0 0
qbGLM 0 0 0
qbGLM_offset 0 0 0
qbGLM_offset_squeeze 0 0 0
Replicate: 4
bGLM 0 5 5
edgeR_NB 0 0 0
edgeR_QP 0 0 0
qbGLM 0 0 0
qbGLM_offset 0 0 0
qbGLM_offset_squeeze 0 0 0
Replicate: 5
bGLM 0 0 2
edgeR_NB 0 0 0
edgeR_QP 0 0 0
qbGLM 0 0 0
qbGLM_offset 0 0 0
qbGLM_offset_squeeze 0 0 0

P-value distributions

pval_figs <- map(res_tables, pval_hist)

n_patients_10

n_patients_20

n_patients_30

P-value distributions stratified by expression level

For each replicate, calculate average log-normalized counts, using scuttle::calculateAverage().

## Calculate average counts for each replicate
avg_counts <- map_depth(sce_objects, 2,
    ~ calculateAverage(counts(.x))
)

## Split res_tables per replicate
res_tables_split <- map(res_tables, ~ split(.x, .x$replicate))

## Add average counts to results tables
## Note that for the 'combined' celltypes, the average counts will be the same
## for each celltype contrast
res_tables_split <- map2(
    res_tables_split, avg_counts,
    ~ map2(.x, .y, function(res, avg_cnt) {
        res$avg_count <- avg_cnt[res$gene]
        res
    })
)

Make p-value histograms stratified by the average counts.

pval_hist_strat_plots <- map(res_tables_split,
    ~ imap(.x, function(y, replicate) {
        replicate <- str_remove(replicate, "replicate_")
        title <- paste("Replicate:", replicate)
        pval_hist_strat(y, n_groups = 6) +
            ggtitle(title)
    })
)

n_patients_10

n_patients_20

n_patients_30

Session info

Session info 
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#> Head:     [57d4ab6] 2023-04-07: update README
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